Multitask Learning for Fundamental Frequency Estimation in Music

Venue

CoRR, vol. abs/1809.00381

Publication Year

2018

Keywords

Computer Science - Machine Learning,Computer Science - Sound,Electrical Engineering and Systems Science - Audio and Speech Processing,Statistics - Machine Learning

Authors

  • Rachel M. Bittner
  • Brian McFee
  • Juan P. Bello

Abstract

Fundamental frequency (f0) estimation from polyphonic music includes the tasks of multiple-f0, melody, vocal, and bass line estimation. Historically these problems have been approached separately, and only recently, using learning-based approaches. We present a multitask deep learning architecture that jointly estimates outputs for various tasks including multiple-f0, melody, vocal and bass line estimation, and is trained using a large, semi-automatically annotated dataset. We show that the multitask model outperforms its single-task counterparts, and explore the effect of various design decisions in our approach, and show that it performs better or at least competitively when compared against strong baseline methods.